CowAgent is an open-source AI assistant and agent framework that plans tasks, uses tools, manages skills, and maintains long-term memory and a personal knowledge base. People run it on a computer or server and connect it to language models, external services, and messaging channels. Its catalogue skills extend the assistant's abilities.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add zhayujie/CowAgent --skill image-generationgit clone --depth 1 https://github.com/zhayujie/CowAgentWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/zhayujie/cowagent/image-generation)<a href="https://agentmods.dev/skills/zhayujie/cowagent/image-generation"><img src="https://agentmods.dev/badge/skills/zhayujie/cowagent/image-generation/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/zhayujie/cowagent/image-generation"><img src="https://agentmods.dev/badge/skills/zhayujie/cowagent/image-generation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00041 | $0.01352 |
| Opus 5.5 | $0.00016 | $0.00541 |
| Sonnet 5.5 | $0.00008 | $0.00270 |
| Haiku 4.5 | $0.00004 | $0.00135 |
Grade A, and why
image-generation scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Image Generation
Generate and edit images using AI models. The script automatically picks a backend based on which API keys are configured — you don't need to specify a model unless the user explicitly names one. Never guess or invent a model: when the user doesn't name one, omit model entirely so the configured default/provider is used.
Supported models (passed via model only when the user asks for a specific one):
- OpenAI —
gpt-image-2.5-flare(recommended default: best quality/latency trade-off),gpt-image-2.5-sunburst(highest editing precision),gpt-image-2,gpt-image-1 - Gemini Nano Banana —
nano-banana-2,nano-banana-pro,nano-banana - Seedream (Volcengine Ark) —
seedream-5.0-lite,seedream-4.5 - Qwen (DashScope) —
qwen-image-2.0,qwen-image-2.0-pro - MiniMax —
image-01
Usage
Run scripts/generate.py with a JSON argument. Use the absolute <base_dir> path — do NOT cd into the skill directory, since it is a builtin skill and gets reset on restart (anything written there is lost).
python <base_dir>/scripts/generate.py '<json_args>'
Images are saved to the workspace (or the open project dir) under images/, so they persist and stay reachable to the client.
Set bash timeout to at least 600 seconds, as image generation can take 30–200s per provider, and the script may try multiple providers sequentially. Call it synchronously (do not run it in the background) and wait for its output in the same turn.
Parameters
| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
prompt |
string | yes | — | Image description |
image_url |
string / list | no | null | Input image(s) for editing: local file path or URL. Multi-image fusion is supported (pass a list) |
quality |
string | no | auto | low / medium / high (only some backends honour this) |
size |
string | no | auto | 512 / 1K / 2K / 3K / 4K, or pixel value (1024x1024) |
aspect_ratio |
string | no | null | 1:1 / 3:2 / 2:3 / 16:9 / 9:16 / 21:9 (some backends also support extreme ratios like 1:4 / 8:1) |
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago Changed b2edcd4daa42
- 1mo ago Changed db986141540f
- 1mo ago First seen · 120 lines · 41 tokens per session scan A 406adec2013a
image-generation is a skill published in the GitHub repository zhayujie/CowAgent (47,507 stars, last pushed yesterday), licensed MIT. It adds 41 tokens to every session and 1,352 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
fastgpt-docker-deploy
FastGPT Docker Compose self-hosting deployment workflow. Use when a user asks an AI agent to deploy FastGPT with Docker by referencing /deploy/SKILL.md, including creating an empty fastgpt directory, running the install script in non-interactive mode, starting Docker Compose, monitoring service health, troubleshooting…
byok-custom-model
Register a custom LLM endpoint with your own API key for chat in Starchild. Use when adding a personal Anthropic, OpenAI, Grok, Qwen, DeepSeek, Meta (Muse Spark), NEAR AI, or Venice key as a chat model (e.g. add my Claude key, register DeepSeek, use Muse Spark 1.1).
playwright
Use when the task requires capturing or automating a real browser from the terminal.
pydantic-ai
Build production-ready AI agents with PydanticAI — type-safe tool use, structured outputs, dependency injection, and multi-model support.
html-artifacts
Author the HTML for a plan artifact, dashboard iframe, or Slack attachment — structure, design plan, available runtime, theming, and craft. Read this before writing HTML for saveplan, outputiframe, or slackattachhtml.
prior-auth-packet-builder
Build a concise prior authorization packet from local case files and payer policy docs.